iiinova — where your digital self keeps evolving

Winston Lin @linstonwin.bsky.social · 5h

This paper by Middleton, Scott, Diakow, & Hill might be of interest too joelmidd.github.io/papers/Middl...

joelmidd.github.io

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Paul Goldsmith-Pinkham @paulgp.com · 10/09/2026

Well, from Organization Science I thought this was pretty remarkable: pubsonline.informs.org/doi/10.1287/...

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Peter Tennant @pwgtennant.bsky.social · 18/08/2026

In nutrition research, the effect you estimate from a model is determined by which other dietary variables you adjust for. If you adjust for total energy intake, the effect will switch from an addition to a substitution. Which substitution will be determined by which other variables are adjusted.

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Peter Tennant @pwgtennant.bsky.social · 18/08/2026

We therefore provide a checklist of recommendations to help future nutrition researchers to conduct clearer and more interpretable meta-analyses of specific effects! Special thanks to lead author @nataliaortega.bsky.social and senior author @georgiatomova.bsky.social!

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David Evans @daveevansphd.bsky.social · 03/06/2026

"The Illusion of Comparability Among Standardised Effect Sizes: Why Education Evaluations Should Report Raw Effects" New @cgdev.org working paper (with Rossiter, Hares, and Henny) cgdev.org/publication/... Summary blog post cgdev.org/blog/standar...

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Jae Yeon Kim @jaeyeonkim.bsky.social · 05/05/2026

When I reflect on what shaped me most as a scholar, one seminar stands out: Ruth Collier’s dissertation writing workshop at Berkeley. I wrote about its influence on my research, teaching, and scholarship in general. jaeyeonkim.substack.com/p/the-ruth-c...

jaeyeonkim.substack.com

The Ruth Collier Method

Think Clearly and Write Honestly

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Winston Lin @linstonwin.bsky.social · 25/04/2026

I admitted it was a little pathetic that I had to reach for smoking & lung cancer Intro to Causal Inference (undergrad stats, Yale, 2021): www.stat.berkeley.edu/~winston/cau... Causal Inference & Research Design (grad political science seminar, Yale, 2019): www.stat.berkeley.edu/~winston/cau...

stat.berkeley.edu

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Winston Lin @linstonwin.bsky.social · 25/04/2026

One student said the class was making her more skeptical about everything, and meant it as a compliment. Toward the end, I said I hoped it'd make them skeptical but not cynical, and many sad movies try to give some hope or uplift at the end, so I had them read about smoking & lung cancer

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Winston Lin @linstonwin.bsky.social · 25/04/2026

I taught abstinence-only causal inference (all reading & writing, no data analysis), though I'm not proud of that! Some of my students had taken @jkalla.bsky.social's class on RCTs, so I said that in contrast to that heartwarming movie, we'd have a heartbreaking movie with observational studies

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Brendan Nyhan @brendannyhan.bsky.social · 23/04/2026

Journal editors - the status quo on preregistration is not working! You need to check submissions vs. preregistrations before sending articles out for review. *56%* of experiments I reviewed in last year have severe problems with non-disclosure, undocumented deviations, & more - see Claude summary ↓

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Ryan Briggs @ryancbriggs.net · 01/04/2026

You guys @carlislerainey.bsky.social has a free textbook online and it seems really useful pos5747.github.io/notes/

A screenshot showing:
Introduction

These are notes for my class on probability models. In these notes, I walk through the concepts and computation that support modern probability modeling in political science using both maximum likelihood and Bayesian approaches.

The Goal

There are many excellent books on probability models. But I felt the need to write my own. Why? I saw three problems.

First, some classes assign a huge textbook. It might be possible for the strongest and most motivated students to become familiar with the range of topics covered in these textbook, but impossible to master. Instead, these textbooks seem like references, something you’re supposed to constantly be referring back to throughout your career. I know this because many of these books have instructors’ guides that suggest what should be covered in a single semester, what should be skipped, and how one might jump around. Instead, I want a book that students can work through beginning to end and master each idea.
Second, some classes assign a variety of sections from several books and a collection of articles. But then the story told in the readings isn’t coherent. The styles are changing, the author’s tastes are changing, and the notation is changing. Switching among authors can feel like whiplash when learning a difficult subject. Instead, I want a book that tells a continuous story with consistent style, tastes, and notation.
Third, some classes assign readings that support the lecture material, without exact alignment between the two. For better or worse, the content covered by the instructor in class feels like the most important material. Thus, I want a book that exactly aligns with the material I cover in class.

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Winston Lin @linstonwin.bsky.social · 01/04/2026

I came across this while trying to refresh my memory about another bilingual joke! A grad school officemate from Montreal told me that at his college, students said something like "Je m'en fiche de la loi de Poisson" or "La loi de Poisson je m'en fiche" 😀

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Winston Lin @linstonwin.bsky.social · 01/04/2026

Bilingual joke? French Wikipedia says the Poisson distribution is "not to be confused with Fisher's distribution" (the F-distribution) fr.wikipedia.org/wiki/Loi_de_...

Screenshot from "Loi de Poisson" (French Wikipedia article). It says, "Ne doit pas être confondu avec Loi de Fisher."

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Winston Lin @linstonwin.bsky.social · 23/03/2026

3) All that's for RCTs. For observational studies, the issues are different, and here's a link to an old favorite paper bsky.app/profile/lins...

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Winston Lin @linstonwin.bsky.social · 23/03/2026

2) Freedman 2008 showed ANCOVA I may have a finite-sample bias. Also true of ANCOVA II. Difference-in-means and difference-in-differences (change scores) are exactly unbiased. Gerber & Green (Field Experiments, p. 104) suggest diff-in-diff with N < 20. Simulations with real data could be useful

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Winston Lin @linstonwin.bsky.social · 23/03/2026

1) Yang & Tsiatis 2001 proved (a) "ANCOVA II" (which I later studied in my 2013 finite-population paper) is asymptotically more efficient than 1 and 3, and (b) with equal sample sizes in treatment & control, 2 ("ANCOVA I") is asymptotically equivalent to ANCOVA II www.jstor.org/stable/2685694

jstor.org

Efficiency Study of Estimators for a Treatment Effect in a Pretest-Posttest Trial on JSTOR

Li Yang, Anastasios A. Tsiatis, Efficiency Study of Estimators for a Treatment Effect in a Pretest-Posttest Trial, The American Statistician, Vol. 55, No. 4 (Nov., 2001), pp. 314-321

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Winston Lin @linstonwin.bsky.social · 20/03/2026

Allison (1990) is helpful for intuition on this (with examples on pp. 97-100 & 109). The assumptions for change scores (diff-in-diff) are different from ANCOVA, but not stronger or weaker, so it depends on the process that determines who gets which treatment statisticalhorizons.com/wp-content/u...

statisticalhorizons.com

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Julia M. Rohrer @dingdingpeng.the100.ci · 25/02/2026

You need to bring in the same toolkit as in studies that try to establish causality without randomization. I know it sounds unfair, but I don’t make the rules. These situations are instances of post-treatment bias, if you want to read up on it as a psychologist:

compass.onlinelibrary.wiley.com

Causal inference for psychologists who think that causal inference is not for them

Correlation does not imply causation and psychologists' causal inference training often focuses on the conclusion that therefore experiments are needed—without much consideration for the causal infer...

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Data Colada @datacolada.bsky.social · 16/02/2026

A more user friendly t-test regression variable description frequency plots, and more. datacolada.org/132

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Andrea Howard @drandreahoward.bsky.social · 06/02/2026

🚨SOLUTIONS🚨 Desk reject more stuff with actionable feedback. Don’t request second reviews Build larger editorial boards of volunteers Wait to submit your work until it’s ready; a.k.a don’t send in your half-baked trash hoping for feedback 6/7

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Winston Lin @linstonwin.bsky.social · 06/02/2026

I’ve thanked people for spending the time to give me comments

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Drew Stommes @drewstommes.bsky.social · 27/01/2026

After years in academia, I’m exploring data science and research roles in industry. I'm a quant. social scientist (PhD Yale ’24, NYU) focused on causal inference, experiments, and large-scale data. Feel free to get in touch or share; all leads appreciated. dwstommes@gmail.com

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Julia M. Rohrer @dingdingpeng.the100.ci · 28/01/2026

This quote also reminds me of something that we wrote in our paper on path analysis (journals.sagepub.com/doi/10.1177/...). People are just expecting *way* too much of a single study, literally new discoveries exceeding Gregor Mendel's.

We believe that to improve practices, some fundamental rethinking of what we consider a publishable scientific contribution may be necessary. Currently, researchers may feel pressured to do “everything” in a single article—summarize and synthesize the existing literature, suggest a new theory or at least modify an existing one, hypothesize moderation and/or mediation, and provide (preferably positive) empirical evidence through statistical analyses that they run themselves, maybe even across multiple studies they conducted themselves. It is perhaps unsurprising that they end up cutting corners when it comes to causal inference—a hard topic, for which psychologists often receive little training—and rely on out-of-the-box statistical models.

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Winston Lin @linstonwin.bsky.social · 30/01/2026

How about Don Campbell and his collaborators, who invented regression discontinuity among other things?

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Jamie Cummins @jamiecummins.bsky.social · 22/01/2026

Comparing registrations to published papers is essential to research integrity - and almost no one does it routinely because it's slow, messy, and time-demanding. RegCheck was built to help make this process easier. Today, we launch RegCheck V2. 🧵 regcheck.app

regcheck.app

RegCheck

RegCheck is an AI tool to compare preregistrations with papers instantly.

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Winston Lin @linstonwin.bsky.social · 19/01/2026

Back in 2017-18, a friend told me that Yale SOM banned laptops in MBA classes

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Winston Lin @linstonwin.bsky.social · 19/01/2026

My syllabi have a footnote recommending the same 2017 @dynarski.bsky.social review that @gregsasso.bsky.social shared. This semester I also looked at Nicholas Decker's recent blog post www.brookings.edu/articles/for... nicholasdecker.substack.com/p/should-we-...

brookings.edu

For better learning in college lectures, lay down the laptop and pick up a pen | Brookings

Susan Dynarski examines the evidence that students learn better if they aren't using their laptops during lectures.

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Winston Lin @linstonwin.bsky.social · 16/01/2026

Rosenbaum, Observation and Experiment

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Julia M. Rohrer @dingdingpeng.the100.ci · 08/01/2026

Accessibility is *absolutely* key but also hard because of the curse of knowledge. I've written down some writing advice here: www.the100.ci/2024/12/01/w.... If you're more of a technical person, consider teaming up with a substantive researcher for instant audience access.>

the100.ci

Writing about technical topics in an accessible manner

A wise man – I’m quite sure it was Brian Wansink – once pointed out that it is impossible to both read and write a lot. So, maybe reading a post about how to write just steals time from the more urgen...

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Julia M. Rohrer @dingdingpeng.the100.ci · 08/01/2026

Some people bring up (1) the cost of criticism and (2) that a lot of criticism has already been voiced but ignored. Both points are valid, so here are some suggestion for (1) reducing backlash and (2) increasing impact (from this talk of mine: juliarohrer.com/wp-content/u...

youtube.com

CIIG Seminar: Julia Rohrer | Making Rigorous Causal Inference More Mainstream | 20 Oct 2025

YouTube video by the Causal Inference Interest Group (CIIG)

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Winston Lin @linstonwin.bsky.social · 27/12/2025

Citations always needed checking! Just as one example, I used to see my sole authored 2013 paper cited as “Lin et al” coz Google Scholar’s bib had an error :)

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Andrew Heiss @andrew.heiss.phd · 09/12/2025

Some closing thoughts for my students this semester on LLMs and learning #rstats datavizf25.classes.andrewheiss.com/news/2025-12...

Will you incorporate LLMs and AI prompting into the course in the future?
No.

Why won’t you incorporate LLMs and AI prompting into the course?
These tools are useful for coding (see this for my personal take on this).

However, they’re only useful if you know what you’re doing first. If you skip the learning-the-process-of-writing-code step and just copy/paste output from ChatGPT, you will not learn. You cannot learn. You cannot improve. You will not understand the code.

In that post, it warns that you cannot use it as a beginner:

…to use Databot effectively and safely, you still need the skills of a data scientist: background and domain knowledge, data analysis expertise, and coding ability.

There is no LLM-based shortcut to those skills. You cannot LLM your way into domain knowledge, data analysis expertise, or coding ability.

The only way to gain domain knowledge, data analysis expertise, and coding ability is to struggle. To get errors. To google those errors. To look over the documentation. To copy/paste your own code and adapt it for different purposes. To explore messy datasets. To struggle to clean those datasets. To spend an hour looking for a missing comma.

This isn’t a form of programming hazing, like “I had to walk to school uphill both ways in the snow and now you must too.” It’s the actual process of learning and growing and developing and improving. You’ve gotta struggle.

This Tumblr post puts it well (it’s about art specifically, but it applies to coding and data analysis too):

Contrary to popular belief the biggest beginner’s roadblock to art isn’t even technical skill it’s frustration tolerance, especially in the age of social media. It hurts and the frustration is endless but you must build the frustration tolerance equivalent to a roach’s capacity to survive a nuclear explosion. That’s how you build on the technical skill. Throw that “won’t even start because I’m afraid it won’t be perfect” shit out the window. Just do it. Just start. Good luck. (The original post has disappeared, but here’s a reblog.)

It’s hard, but struggling is the only way to learn anything.

You might not enjoy code as much as Williams does (or I do), but there’s still value in maintaining codings skills as you improve and learn more. You don’t want your skills to atrophy.

As I discuss here, when I do use LLMs for coding-related tasks, I purposely throw as much friction into the process as possible:

To avoid falling into over-reliance on LLM-assisted code help, I add as much friction into my workflow as possible. I only use GitHub Copilot and Claude in the browser, not through the chat sidebar in Positron or Visual Studio Code. I treat the code it generates like random answers from StackOverflow or blog posts and generally rewrite it completely. I disable the inline LLM-based auto complete in text editors. For routine tasks like generating {roxygen2} documentation scaffolding for functions, I use the {chores} package, which requires a bunch of pointing and clicking to use.

Even though I use Positron, I purposely do not use either Positron Assistant or Databot. I have them disabled.

So in the end, for pedagogical reasons, I don’t foresee me incorporating LLMs into this class. I’m pedagogically opposed to it. I’m facing all sorts of external pressure to do it, but I’m resisting.

You’ve got to learn first.

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Winston Lin @linstonwin.bsky.social · 05/12/2025

I think of the 2019 Nobel as the 2nd wave of the experimental part of the credibility revolution. Ashenfelter, Card, & Lalonde’s work led to major job training RCTs in the US, and Angrist was one of Duflo’s advisors. Ashenfelter has a nice speech on the early history legacy.iza.org/en/webconten...

legacy.iza.org

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Winston Lin @linstonwin.bsky.social · 22/11/2025

I like this from @vamrhein.bsky.social et al. I assigned it to my class last semester and tried to explain that p-values measure how compatible (vs. surprising) the data are with the null, given our assumptions. But yeah, tests & CIs are hard to understand! www.blakemcshane.com/Papers/natur...

from Amrhein, Greenland, & McShane ("Retire statistical significance," Nature, 2019)

"For example, the authors above could have written: ‘Like a previous study, our results suggest a 20% increase in risk of new-onset atrial fibrillation in patients given the anti-inflammatory drugs. Nonetheless, a risk difference ranging from a 3% decrease, a small negative association, to a 48% increase, a substantial positive association, is also reasonably compatible with our data, given our assumptions.’ "

from Amrhein, Greenland, & McShane ("Retire statistical significance," Nature, 2019)

"Whatever the statistics show, it is fine to suggest reasons for your results, but discuss a range of potential explanations, not just favoured ones. Inferences should be scientific, and that goes far beyond the merely statistical. Factors such as background evidence, study design, data quality and understanding of underlying mechanisms are often more important than statistical measures such as P values or intervals."

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John V. Kane @uptonorwell.bsky.social · 24/09/2025

Are you or one of your students considering doing a Ph.D. in a social science? I've spent a lot of time talking about this w/ students & finally wrote something up. IMO, there are only 3 good reasons to do it. One of them needs to be true--otherwise, don't. medium.com/the-quantast...

medium.com

The Only Three Reasons to Do a Ph.D. in the Social Sciences

If none are true, don’t do it.

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Coalition for Evidence-Based Policy @coalition4evidence.bsky.social · 11/06/2025

See our No-Spin report on a widely-covered NBER study of Medicaid expansion. In brief: Despite the abstract's claims that expansion reduced adult mortality 2.5%, the study found much smaller effects that fell short of statistical significance in its main preregistered analysis.🧵

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Noah Greifer @noahgreifer.bsky.social · 04/06/2025

Starting to look like I might not be able to work at Harvard anymore due to recent funding cuts. If you know of any open statistical consulting positions that support remote work or are NYC-based, please reach out! 😅

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Winston Lin @linstonwin.bsky.social · 05/05/2025

In case this is of interest, even ANCOVA I is consistent and asymptotically normal in completely randomized experiments (though II is asymptotically more efficient in imbalanced or multiarm designs)

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Ethan Mollick @emollick.bsky.social · 21/04/2025

Issues with interpreting p-values haunts even AI, which is prone to same biases as human researchers. ChatGPT, Gemini & Claude all fall prey to "dichotomania" - treating p=0.049 & p=0.051 as categorically different, and paying too much attention to significance. www.cambridge.org/core/journal...

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Brennan Kahan @brennankahan.bsky.social · 15/04/2025

NEW: CONSORT 2025 now published! Some notable changes: -items on analysis populations, missing data methods, and sensitivity analyses -reporting of non-adherence and concomitant care -reporting of changes to any study methods, not just outcomes -and lots of other things www.bmj.com/content/389/...

bmj.com

CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials

Critical appraisal of the quality of randomised trials is possible only if their design, conduct, analysis, and results are completely and accurately reported. Without transparent reporting of the met...

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